
To further evaluate the performance of our model, we visualize the temporal predictions of the models. As presented in Figure REF , our model exactly localizes the anomalous events and predicts anomaly scores very close to zero on normal videos, showing the effectiveness and robustness of our model. We collect some failed samples in the right row of Figure REF . In addition, our model predicts the highest score at the end of Arrest001, where a man walks across the scene with his arm pointing forward as if brandishing a gun. [rgb]0,0, 0As the videos in UCF-Crime are low-resolution, it is difficult to judge such a confusing action without any other context information.
Furthermore, the bottom-right part of Figure REF 
shows another failed case; , our model successfully localizes the major part of the anomalous burglary event and raises an alarm when the thieves are rushing out of the house, which should be treated as an anomaly but is wrongly labeled as a normal event in the ground truth.
We also visualize the spatial activation map via Grad-CAM [rgb]0,0,0on \(\mathcal {M}_{\mathcal {A}}\)  [1]} for spatial explanation. As Figure REF  shows, our model is able to sensitively focus on informative regions that help decide whether the scene is anomalous . [rgb]0,0, 0This verifies that our self-guided attention module can boost the feature encoder to focus on anomalous regions. [rgb]0.,0.,0Additionally, compared with the activation maps generated from the MIST without guided-classification head \(H_g\)  and the MIST without the SGA module, the results of MIST are concentrated on the anomalous regions, which shows the rationality and effectiveness of our self-guided attention module.
